


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase     		=							'true'
def 							UpperCamelCase_(					_A :Any					,  _A :int=82					,  _A :Union[str, Any]=16	)->		List[Any]:
			set_seed(42	)
			UpperCamelCase__       =							RegressionModel()
			UpperCamelCase__       =							deepcopy(_A	)
			UpperCamelCase__       =							RegressionDataset(length=_A	)
			UpperCamelCase__       =							DataLoader(_A					,  batch_size=_A	)
			model.to(accelerator.device	)
			UpperCamelCase__,   UpperCamelCase__       =							accelerator.prepare(_A					,  _A	)
			return model, ddp_model, dataloader
def 							UpperCamelCase_(					_A :Accelerator					,  _A :Dict=False	)->		Optional[Any]:
			UpperCamelCase__       =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
			UpperCamelCase__       =							load_dataset("glue"					,  "mrpc"					,  split="validation"	)

			def tokenize_function(_A :str	):
						UpperCamelCase__       =							tokenizer(examples["sentence1"]					,  examples["sentence2"]					,  truncation=_A					,  max_length=_A	)
						return outputs

			with accelerator.main_process_first():
						UpperCamelCase__       =							dataset.map(
						    _A					,  batched=_A					,  remove_columns=["idx", "sentence1", "sentence2"]					,  )

			UpperCamelCase__       =							tokenized_datasets.rename_column("label"					,  "labels"	)

			def collate_fn(_A :str	):
						if use_longest:
									return tokenizer.pad(_A					,  padding="longest"					,  return_tensors="pt"	)
						return tokenizer.pad(_A					,  padding="max_length"					,  max_length=1_28					,  return_tensors="pt"	)

			return DataLoader(_A					,  shuffle=_A					,  collate_fn=_A					,  batch_size=16	)
def 							UpperCamelCase_(					_A :int					,  _A :Any	)->		List[str]:
			UpperCamelCase__       =							Accelerator(dispatch_batches=_A					,  split_batches=_A	)
			UpperCamelCase__       =							get_dataloader(_A					,  not dispatch_batches	)
			UpperCamelCase__       =							AutoModelForSequenceClassification.from_pretrained(
			    "hf-internal-testing/mrpc-bert-base-cased"					,  return_dict=_A	)
			UpperCamelCase__,   UpperCamelCase__       =							accelerator.prepare(_A					,  _A	)
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 							UpperCamelCase_(					_A :List[str]					,  _A :List[str]					,  _A :Optional[int]	)->		str:
			UpperCamelCase__       =							[]
			for batch in dataloader:
						UpperCamelCase__,   UpperCamelCase__       =							batch.values()
						with torch.no_grad():
									UpperCamelCase__       =							model(_A	)
									UpperCamelCase__,   UpperCamelCase__       =							accelerator.gather_for_metrics((logit, target)	)
									logits_and_targets.append((logit, target)	)
			UpperCamelCase__,   UpperCamelCase__       =							[], []
			for logit, targ in logits_and_targets:
						logits.append(_A	)
						targs.append(_A	)
			UpperCamelCase__,   UpperCamelCase__       =							torch.cat(_A	), torch.cat(_A	)
			return logits, targs
def 							UpperCamelCase_(					_A :Accelerator					,  _A :Optional[int]=82					,  _A :Dict=False					,  _A :Dict=False					,  _A :Any=16	)->		Any:
			UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							get_basic_setup(_A					,  _A					,  _A	)
			UpperCamelCase__,   UpperCamelCase__       =							generate_predictions(_A					,  _A					,  _A	)
			assert (
			    len(_A	) == num_samples
			), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_A	)}'''
def 							UpperCamelCase_(					_A :bool = False					,  _A :bool = False	)->		int:
			UpperCamelCase__       =							evaluate.load("glue"					,  "mrpc"	)
			UpperCamelCase__,   UpperCamelCase__       =							get_mrpc_setup(_A					,  _A	)
			# First do baseline
			UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							setup["no"]
			model.to(_A	)
			model.eval()
			for batch in dataloader:
						batch.to(_A	)
						with torch.inference_mode():
									UpperCamelCase__       =							model(**_A	)
						UpperCamelCase__       =							outputs.logits.argmax(dim=-1	)
						metric.add_batch(predictions=_A					,  references=batch["labels"]	)
			UpperCamelCase__       =							metric.compute()

			# Then do distributed
			UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							setup["ddp"]
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									UpperCamelCase__       =							model(**_A	)
						UpperCamelCase__       =							outputs.logits.argmax(dim=-1	)
						UpperCamelCase__       =							batch["labels"]
						UpperCamelCase__,   UpperCamelCase__       =							accelerator.gather_for_metrics((preds, references)	)
						metric.add_batch(predictions=_A					,  references=_A	)
			UpperCamelCase__       =							metric.compute()

			for key in "accuracy f1".split():
						assert math.isclose(
						    baseline[key]					,  distributed[key]	), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def 							UpperCamelCase_(					)->		Optional[int]:
			UpperCamelCase__       =							Accelerator(split_batches=_A					,  dispatch_batches=_A	)
			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_warning()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			# These are a bit slower so they should only be ran on the GPU or TPU
			if torch.cuda.is_available() or is_tpu_available():
						if accelerator.is_local_main_process:
									print("**Testing gather_for_metrics**"	)
						for split_batches in [True, False]:
									for dispatch_batches in [True, False]:
												if accelerator.is_local_main_process:
															print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''	)
												test_mrpc(_A					,  _A	)
												accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print("**Test torch metrics**"	)
			for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
									UpperCamelCase__       =							Accelerator(split_batches=_A					,  dispatch_batches=_A	)
									if accelerator.is_local_main_process:
												print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''	)
									test_torch_metrics(_A					,  99	)
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print("**Test last batch is not dropped when perfectly divisible**"	)
			UpperCamelCase__       =							Accelerator()
			test_torch_metrics(_A					,  5_12	)
			accelerator.state._reset_state()
def 							UpperCamelCase_(					_A :List[str]	)->		int:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
						main()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


